Elastic net (Enet) and sparse partial least squares (SPLS) are frequently employed for wavelength selection and model calibration in analysis of near infrared spectroscopy data. Enet and SPLS can perform variable selection and model calibration simultaneously. And they also tend to select wavelength intervals rather than individual wavelengths when the predictors are multicollinear. In this paper, we focus on comparison of Enet and SPLS in interval wavelength selection and model calibration for near infrared spectroscopy data. The results from both simulation and real spectroscopy data show that Enet method tends to select less predictors as key variables than SPLS; thus it gets more parsimony model and brings advantages for model interpretation. SPLS can obtain much lower mean square of prediction error (MSE) than Enet. So SPLS is more suitable when the attention is to get better model fitting accuracy. The above conclusion is still held when coming to performing the strongly correlated NIR spectroscopy data whose predictors present group structures, Enet exhibits more sparse property than SPLS, and the selected predictors (wavelengths) are segmentally successive.

中文翻译：

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基于NIR光谱数据的波长选择中的稀疏偏最小二乘和弹性网的比较。

弹性网（Enet）和稀疏偏最小二乘（SPLS）通常用于近红外光谱数据分析中的波长选择和模型校准。Enet和SPLS可以同时执行变量选择和模型校准。而且，当预测变量为多重共线性时，它们还倾向于选择波长间隔而不是单个波长。在本文中，我们将重点放在Enet和SPLS在间隔波长选择和近红外光谱数据模型校准方面的比较。模拟和实际光谱数据的结果表明，与SPLS相比，Enet方法倾向于选择较少的预测变量作为关键变量。因此，它获得了更多的简约模型，并为模型解释带来了优势。SPLS可以获得比Enet低得多的预测误差均方根（MSE）。因此，SPLS更适合用于获得更好的模型拟合精度的情况。当进行预测因子呈现群结构的强相关NIR光谱数据时，上述结论仍然成立，Enet的稀疏性比SPLS稀疏，所选的预测因子（波长）是分段连续的。